Taxa bar plots - should we use normalized data?

[fstudart (Fernando Studart)]

Hi Everyone,

I know that for alpha and beta-diversity analyses we should normalize the data. For these analyses, this is pretty straightforward, as the qiime diversity core-metrics-phylogenetic plugin includes an option to do this: --p-sampling-depth.

I’d like to make, for instance, a taxa bar plot showing the top 10 most abundant genera across my samples. For that, I’m importing the BIOM file (from feature table) and making some plots in R using Phyloseq.

My question is: Which BIOM file would be the most appropriate in that case?

1. The BIOM file created from feature table (which has non-normalized data). This is the same feature table I have used with the qiime taxa barplot plugin.

2. Normalized BIOM file. To create this file, I’ve used the single_rarefaction.py plugin from Qiime1 with the same sampling depth I used for the alpha and beta-diversity analyses.

Actually, I have used both approaches. The plots looked similar, but there are some changes, as expected, in terms of the relative abundance (RA) of some taxa, specially at the genus level. If I wanna compare the RA of a specific genus according to a a metadata category, which approach would be the most correct?

Thanks in advance,
FS

[gregcaporaso]

Hi @fstudart,
You should use table (1) that you’re describing, though I’m not surprised that you see similar results with both of those tables. The table will be normalized to relative abundances during the creation of this visualization. One step that is often worth taking is filtering out samples with very low total frequencies (i.e., sequence counts) before generating these tables (see here for instructions on how to apply that filter) - what that minimum total frequency is depends on your data, but if it’s Illumina data, around 1000 is probably a reasonable choice.

[fstudart (Fernando Studart)]

Hi Dr. Caporaso,

Thanks for the explanation.

Thanks very much for all the support we’ve been receiving here at the Qiime2 forum.

FS